Shared automated vehicles are expected to be part of the supply of transportation systems in the future. Parallel to this evolution, there is the rapid penetration of battery electric vehicles (BEVs). The limitations in battery capacity and charging speed of BEVs can influence the planning and operation of shared automated electric vehicle (SAEV) systems. The design of such systems needs to include these limitations so that their viability is properly estimated. In this paper, we develop a space–time-energy flow-based integer programming (IP) model in support of the strategic design of a regional SAEV system. The proposed approach optimizes the fleet (size and composition) and charging facilities (number and location), while explicitly accounting for vehicle operations in aggregated terms (including movements with users, relocations, and charging times). The model is used to assess the impact of vehicle range and different types of chargers in the optimal design of an interurban SAEV transport system in the center of Portugal. Results show a reduction in profit as the vehicle range increases. In regards to energy, it is observed that the adoption of long-range vehicles reduces the energy spent in relocations, and increases the amount of energy charged at a lower price. Additionally, it is found that a system with long-range vehicles does not take advantage of having fast chargers. Concerning the chargers’ optimal location, systems using short-range vehicles have more chargers close to the main commuter trips attracting cities, while systems with long-range vehicles have the chargers nearby the homes of users.@en

The future of road transportation systems faces fundamental changes concerning technological progress and business models. Automated and electric vehicles are coming into the market and evolving towards a service-based mobility system with promises to tackle energy and environmental issues in the mobility sector. Although recent studies have begun to explore the potential impact of shared and privately owned automated and electric vehicles (AEVs) mostly from an operational perspective, little is known about the life cycle impact of such future transport systems. To fill this gap, this paper aims to compare the life cycle environmental impacts of shared vs privately owned AEVs in a regional context. A life cycle assessment (LCA) approach is developed to appraise impact categories with a direct effect on human health, ecosystems, and resources availability. Given that automated vehicles are not yet being used massively, the LCA is applied to synthetic travel demand data to assess the characteristics of privately-owned AEVs and the results of an optimization model that determines the vehicle fleet and driving patterns of shared AEVs serving a regional case-study in the central region of Portugal. Two different vehicle seating capacities - one passenger (non-ridesharing) and four passengers (ridesharing) – are considered to evaluate shared mobility systems. Results show that shared mobility systems yield a potential reduction of up to 42% (with 4 passengers per vehicle) of the system's environmental impacts compared to privately owned automated vehicles. Human toxicity, mineral resource scarcity, and marine and freshwater ecotoxicity are the impact categories with a higher potential of reduction.

@en

Shared automated vehicles (SAV) are expected to become part of on-demand transport systems in the future. In an interurban context, SAV systems may potentially contribute to significantly improve the availability and quality of public transport. In this paper, we propose a flow-based integer programming approach to design and assess the financial viability of a SAV interurban system. An integer programming model, based on profit maximization and allowing vehicle pooling, was developed to optimize the fleet size and vehicle movements of a typical working day. This model and its optimal results were the basis for assessing the financial viability of an interurban transport network in the central area of Portugal. Different levels of demand were tested and three different fleet compositions were considered: cars, minibuses, and a mixed fleet of both. The results showed that a mixed fleet of vehicles is the best option for profit maximization. The optimal share of each vehicle type, in this fleet composition, asymptotically converges to approximately 1/3 of cars and 2/3 of minibuses, as demand increases. The introduction of a SAV system in such a region for interurban trips can replace up to 18 private vehicles per SAV vehicle, and should be done with sequential expansions to guarantee a profit at the different stages. The break-even point, for the conditions of the case study, is between 1.5 and 2 passengers per vehicle, for a fleet of cars, and between 4 and 5 passengers per vehicle, for a fleet of minibuses.

@en

This paper proposes a real-time decision support tool based on the rolling-horizon principle that manages staff activities (relocations and maintenance) of a one-way carsharing system and considers carpooling the staff in the relocated carsharing vehicles for extra cost reduction. The decision support tool is composed of three elements: a forecasting model, an assignment model and a filter. Two assignment models are proposed and tested: rule-based and optimization. The rule-based model uses simple rules to respond to system status changes, and the optimization model is a mixed integer programing (MIP) model prepared to work in real-time. A simulator was designed to test the decision support tool and an application is done to the city of Lisbon, Portugal, showing that the benefits of staff relocations can be rather low. It was verified that the number of relocations that can physically be performed by each staff member in the case study provide only a small improvement in the revenues, which is unlikely to overcome the costs associated with hiring and staff activity.@en